Two-stage hospital efficiency analysis including qualitative evidence: A Greek case
Bibliographic record
Abstract
Background: The European Union health policy agenda stresses the importance of environmental and qualitative factors in structural hospital reforms. In response to the economic crisis, both cost containment and performance improvements of the Greek hospital sector, have become a pertinent issue for overall reforms.Objective: The study examines the efficiency of 112 Greek public hospitals, by applying bootstrapping techniques and investigating the effect of contextual factors on hospital efficiency. Furthermore, the effect of qualitative evidence, on hospital efficiency is explored by focusing on a subset of 28 large hospitals.Methods: The quality aspects of the Greek hospitals are investigated by applying two models of Data Envelopment Analysis (DEA), augmented by bootstrapping techniques, in order to assess the importance of quality dimensions on the efficiency of hospital scores. In addition, two Tobit regression models are estimated assessing the contribution of contextual factors, in the efficiency and bias-corrected efficiency scores.Results: Efficiency analysis indicated that only 23.2% of the hospitals are fully efficient (0.96-1.00), 37.5% are efficient (0.71-0.95) while 39.3% are inefficient (0.30-0.70). The Kolmogorov-Smirnov test, between the original and the bootstrap-corrected efficiency, indicates that their distributions are significantly different (p-value < .001). The environmental factors, influencing efficiency, are Occupancy Rate and the ratio between Outpatient Visits and Inpatient Days. Results indicate that the inclusion of Risk-Adjustment Mortality Rate significantly influences (p-value < .05) the efficiency of the hospitals.Conclusions: In the era of economic crisis, the inclusion of quality variables and the use of bootstrapping techniques provide a vital framework in assessing the efficiency of the hospital sector.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".